Authorship Does Not Decide Substance: An Outcome-Based Framework for Intellectual Products

Suppose AI participation in producing a work is reliably recorded. Suppose further that a named human intellectually directed the work and accountably adopted it. Neither fact establishes whether the resulting product contains intellectual substance. This technical note proposes an outcome-based framework for evaluating intellectual products: eight tests in four tiers with an explicit decision rule, covering the determinacy of the product's content, its boundaries, the constraints it accepts, the consequences it generates, its operationalization, its openness to independent examination, its corrigibility, and its capacity for transfer and durable use. The framework applies symmetrically to human-produced, AI-assisted, collaborative, and institutional work. Production method, polish, prestige, peer review, institutional affiliation, and volume are treated as evidence for other questions, never as verdicts on substance. The decision rule yields five ordered findings: presently unassessable, substance not established, hollow, substantive, and substantive asset. Hollowness is defined narrowly, as apparent intellectual density that materially exceeds determinate, constraint-bearing content, and is procedurally guarded: it is a conclusion of the tests, never a starting label. Production history may establish participation or compliance with a separately defined duty. It cannot substitute for evaluating the product itself. This is the third technical note in a series. Detection Is Not Provenance (https://doi.org/10.5281/zenodo.21881430) limited what a detector estimate establishes about production. Provenance Does Not Decide Authorship (https://doi.org/10.5281/zenodo.21890924) granted a reliable production record and limited what it establishes about authorship. This paper grants both results and addresses the question that remains: whether the product contains intellectual substance. The deposit contains the paper (v1.0) and a companion document, Sources and Verification Method, which records the sources behind the paper's references and the verification performed on each, the positions the paper considered and rejected, the principal editorial choices, the reflexive application of the framework to the paper itself, and the limits of that record. Disclosure. The author is founder and CEO of FERZ, Inc., which develops deterministic governance infrastructure for AI systems. The framework proposed here is general and does not describe or evaluate a FERZ product. Citing this work. The version DOI https://doi.org/10.5281/zenodo.21892670 resolves to this release and its files as deposited. The concept DOI https://doi.org/10.5281/zenodo.21892669 resolves to the latest version and is the right identifier when referring to the work in general.